Multimodal Exponentially Modified Gaussian Oscillators

Acoustic modeling serves audio processing tasks such as de-noising, data reconstruction, model-based testing and classification. Previous work dealt with signal parameterization of wave envelopes either by multiple Gaussian distributions or a single asymmetric Gaussian curve, which both fall short in representing super-imposed echoes sufficiently well. This study presents a three-stage Multimodal Exponentially Modified Gaussian (MEMG) model with an optional oscillating term that regards captured echoes as a superposition of univariate probability distributions in the temporal domain. With this, synthetic ultrasound signals suffering from artifacts can be fully recovered, which is backed by quantitative assessment. Real data experimentation is carried out to demonstrate the classification capability of the acquired features with object reflections being detected at different points in time. The code is available at https://github.com/hahnec/multimodal_emg.Comment: IEEE International Ultrasonic Symposium 202

A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification

One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds generally involve more sound sources while exhibiting less structure in temporal spectral representations. However, the background of an acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting it could be characterized by distribution statistics rather than temporal details. In this work, we investigated using auditory summary statistics as the feature for ASC tasks. The inspiration comes from a recent neuroscience study, which shows the human auditory system tends to perceive sound textures through time-averaged statistics. Based on these statistics, we further proposed to use linear discriminant analysis to eliminate redundancies among these statistics while keeping the discriminative information, providing an extreme com-pact representation for acoustic scenes. Experimental results show the outstanding performance of the proposed feature over the conventional handcrafted features.Comment: Accepted as a conference paper of Interspeech 201

Enhanced visualisation of dance performance from automatically synchronised multimodal recordings

The Huawei/3DLife Grand Challenge Dataset provides multimodal recordings of Salsa dancing, consisting of audiovisual streams along with depth maps and inertial measurements. In this paper, we propose a system for augmented reality-based evaluations of Salsa dancer performances. An essential step for such a system is the automatic temporal synchronisation of the multiple modalities captured from different sensors, for which we propose efficient solutions. Furthermore, we contribute modules for the automatic analysis of dance performances and present an original software application, specifically designed for the evaluation scenario considered, which enables an enhanced dance visualisation experience, through the augmentation of the original media with the results of our automatic analyses

Estimation of Severity of Speech Disability through Speech Envelope

In this paper, envelope detection of speech is discussed to distinguish the pathological cases of speech disabled children. The speech signal samples of children of age between five to eight years are considered for the present study. These speech signals are digitized and are used to determine the speech envelope. The envelope is subjected to ratio mean analysis to estimate the disability. This analysis is conducted on ten speech signal samples which are related to both place of articulation and manner of articulation. Overall speech disability of a pathological subject is estimated based on the results of above analysis.Comment: 8 pages,4 Figures,Signal & Image Processing Journal AIRC

Gabor frames and deep scattering networks in audio processing

This paper introduces Gabor scattering, a feature extractor based on Gabor frames and Mallat's scattering transform. By using a simple signal model for audio signals specific properties of Gabor scattering are studied. It is shown that for each layer, specific invariances to certain signal characteristics occur. Furthermore, deformation stability of the coefficient vector generated by the feature extractor is derived by using a decoupling technique which exploits the contractivity of general scattering networks. Deformations are introduced as changes in spectral shape and frequency modulation. The theoretical results are illustrated by numerical examples and experiments. Numerical evidence is given by evaluation on a synthetic and a "real" data set, that the invariances encoded by the Gabor scattering transform lead to higher performance in comparison with just using Gabor transform, especially when few training samples are available.Comment: 26 pages, 8 figures, 4 tables. Repository for reproducibility: https://gitlab.com/hararticles/gs-gt . Keywords: machine learning; scattering transform; Gabor transform; deep learning; time-frequency analysis; CNN. Accepted and published after peer revisio

Visually Indicated Sounds

Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene. We present an algorithm that synthesizes sound from silent videos of people hitting and scratching objects with a drumstick. This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions

Ambient Sound Provides Supervision for Visual Learning

The sound of crashing waves, the roar of fast-moving cars -- sound conveys important information about the objects in our surroundings. In this work, we show that ambient sounds can be used as a supervisory signal for learning visual models. To demonstrate this, we train a convolutional neural network to predict a statistical summary of the sound associated with a video frame. We show that, through this process, the network learns a representation that conveys information about objects and scenes. We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds.Comment: ECCV 201